The human retina offers a window into the health of a person. Retinal microvasculature is the only part of the human circulation that can be visualized non-invasively in vivo, readily photographed and quantitatively analyzed. The content of a retinal image with high resolution structure of photoreceptors and vascular flow offers detailed information that would enable doctors to make early and accurate diagnosis of diseases. Clinical signs discernible in retinal fundus images include microaneurysms, dot and blot hemorrhages, exudates and intra-retinal micro-vascular abnormalities. Such retinal disorders impose serious attacks to the eye which may lead to the loss of vision or even blindness. Some retinal diseases are even indications of systemic diseases such as diabetes, hypertension and arteriosclerosis. For example, Diabetic Retinopathy (DR) is a common complication of diabetes that damages the eye's retina. Therefore, the early diagnosis of vision threatening retinal diseases is important to the timely treatment to minimize further damages and deterioration.
Clinical signs used by medical specialists to diagnose retinal diseases are based on the examination of delicate structure of retina and its changes. Retinal imaging which combines computing and electronic imaging has emerged as an innovative technology to be used in large-scale screening programs with significant resource savings, free from observer bias and reliable aid in clinical decision-making. However, automated retinal image analysis is complicated by the fact that retinal features can vary from large vessels to tiny dots, and that the local contrast of retinal structure is unstable, especially in unhealthy ocular fundus. In addition, it is very time consuming to examine a large number of feature segments in retinal images. The success of a retinal imaging system depends on four key issues: 1) quality of retinal images (data acquisition), 2) effectiveness of retinal feature extraction and representation, 3) accuracy and efficiency to identify retinal abnormalities (mass screening and diagnosis) and 4) reliability to detect and classify changes in time series of retinal images (monitoring of the disease over time). It is a challenging task to develop a high performance retinal imaging system for computer aided medical diagnosis and monitoring with respect to robustness, accuracy, efficiency, safety and cost. The following is the fundamental issues existing in the art:
(a) Image Acquisition: how to capture good quality retinal images with flexibility and at low cost?
(b) Image Representation: how to extract multiple retinal features in different spectrum?
(c) Diagnosis/Screening: how to identify changes of retinal feature structures within individual image samples?
(d) Monitoring: Has any change occurred over a time series of retinal images?